knitr::opts_chunk$set(fig.width = 6, fig.height = 4, fig.path = 'Figs/',
                      echo = FALSE, message = FALSE, warning = FALSE)

dir_git     <- path.expand('~/github/ohibc')
source(file.path(dir_git, 'src/R/common.R'))  
dir_spatial <- file.path(dir_git, 'prep/_spatial')  
dir_anx     <- file.path(dir_M, 'git-annex/bcprep')


### goal specific folders and info
goal      <- 'fis'
scenario  <- 'v2017'
dir_goal  <- file.path(dir_git, 'prep', goal, scenario)
dir_goal_anx <- file.path(dir_anx, goal, scenario)

### provenance tracking
library(provRmd); prov_setup()

### Kobe plot functions
source(file.path(dir_goal, 'kobe_fxns.R'))
library(plotly)

There are 13 RAM stocks used for the FIS sub-goal.

RAM metrics for each stock

Catch over time for each stock

The catch values here come from the RAM database as well.

Remove tuna

Contribution of regional catch by stock

Breaking this up by region and stock

Tuna

Looking just at albacore tuna, the offshore region is entirely dependent on Albacore tuna while this stock makes up a very small portion of the catch elsewhere.

B/Bmsy estimates from the global CMSY data

Pulling in the B/Bmsy estimates for stocks in FAO area 67 from this year (2017) for consideration in the model.

How much catch is reported for BC stocks in RAM? And of that catch, how much do these stocks contribute to the total?

Plot total catch assessed vs unassessed in a stacked bar chart

Let’s look at how catch changes by species over time using the SAUP data.

Match SAUP data to RAM data to get a better picture of how much catch is assessed vs not assessed

Change to catch prop

Can we parse out the SAUP catch to the sub-regional level?